Synchronization-based approach for detecting functional activation of brain

Abstract

In this paper, we investigate a synchronization-based, data-driven clustering approach for the analysis of functional magnetic resonance imaging (fMRI) data, and specifically for detecting functional activation from fMRI data. We first define a new measure of similarity between all pairs of data points (i.e., time series of voxels) integrating both complete phase synchronization and amplitude correlation. These pairwise similarities are taken as the coupling between a set of Kuramoto oscillators, which in turn evolve according to a nearest-neighbor rule. As the network evolves, similar data points naturally synchronize with each other, and distinct clusters will emerge. The clustering behavior of the interaction network of the coupled oscillators, therefore, mirrors the clustering property of the original multiple time series. The clustered regions whose cross-correlation coefficients are much greater than other regions are considered as the functionally activated brain regions. The analysis of fMRI data in auditory and visual areas shows that the recognized brain functional activations are in complete correspondence with those from the general linear model of statistical parametric mapping, but with a significantly lower time complexity. We further compare our results with those from traditional K-means approach, and find that our new clustering approach can distinguish between different response patterns more accurately and efficiently than the K-means approach, and therefore more suitable in detecting functional activation from event-related experimental fMRI data.

The Kuramoto model in complex networks

“…6). Finally, we would like to mention that the Kuramoto model in complex networks has been considered in many other applications, such as machine learning [414][415][416][417][418][419], characterization of financial market networks [420], modelling of groups of animals in motion [421], oscillatory dynamics of cell networks [422], logistics [423][424][425] and opinion dynamics [426][427][428], besides being employed in the methods for community detection in networks based on phase oscillators discussed in Sec. 3.3.…”

Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behavior

“…Complex network theory [8][9][10][11][12][13][14][15][16][17][18] has undergone an explosive growth in recent years. In particular, complex network analysis of time series 19 has been well developed and it contributes greatly to solve challenging problems in different research fields.…”

A novel complex network-based deep learning method for characterizing gas–liquid two-phase flow

“…Its main idea is to map a real-world system to a network, where the nodes denote the components of the system and the edges allow describing the relationship among these components. It provides an effective framework for better understanding of complex systems and has made great progress in various fields (Newman 2003;Zhang et al 2008;Xu et al 2008;Donges et al 2011;Hong et al 2012;Wang et al 2016;Gao et al 2018). In particular, complex network theory can effectively extract topological characteristics from the time series.…”